Artificial intelligence (AI) is the theory and development of computer systems able to perform tasks that normally require human intelligence. This includes visual perception and pattern recognition, speech recognition, decision-making, natural language processing and translation. Machine learning is the branch of AI in which computers learn from data without human assistance. Deep learning is a type of machine learning that trains a computer to perform human-like tasks such as recognizing speech, identifying images, or making predictions. AI refers to the simulation of human intelligence processes by machines, particularly computer systems with appropriate hardware and software. It involves creating systems that can perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, making decisions, solving problems and learning from experience. AI encompasses a wide range of technologies, algorithms and methodologies, each serving different purposes. In recent years, AI has been rapidly emerging in areas such as computer vision, generative AI with large language models, etc. AI in computer vision has found relevant use cases in quality inspection. Neural-network-based deep learning models have demonstrated high accuracy in object detection and classification in the area of digital image processing. As AI models start to show 
great potential to replace human cognition in quality inspection process through object detection and classification, AI-assisted quality inspection promises to further automate these processes. While this white paper focuses on the application of computer vision AI for automating inspection (i.e., applying AI for pattern recognition on inspection images), the rapidly growing availability and maturity of generative AI presents future possibility in generating inspection criteria.

IPC International, Inc. recognizes the challenges and opportunities facing companies in the electronics industry in the evolving practice of sustainability, most notably, the obligations established by the Corporate Sustainability Reporting Directive (CSRD). That is why IPC is dedicated to empowering the industry with guidance and resources to assist in navigating these obligations, including double materiality assessments (DMAs). DMAs oblige companies to identify and assess their actual or potential impacts related to environmental, societal, and economic sustainability matters (impact materiality), and sustainability matters that affect or may affect their current or future financial performance (financial materiality). A DMA is performed by a company to identify which sustainability topics are most important – or material – to its operations and stakeholders; these topics are evaluated and included in a company’s CSRD report. The purpose of this white paper is to help companies to prepare for achieving their DMA obligations.

As the United States turns to generative artificial intelligence (AI) to drive a wide range of manufacturing applications, it is critical to ensure the strength of assembly capabilities to remain competitive in the global marketplace. These applications leverage the high computational power, data processing capabilities, and machine learning models AI-based servers provide. The AI server market is expected to grow rapidly, with the biggest change coming from accelerated servers, including GPU servers. This whitepaper serves as a blueprint for strengthening assembly capabilities in the United States, particularly in printed circuit board assembly (PCBA) capability and capacity. Topics covered include key technologies, important applications, and global and regional supply chains needed to produce AI-based servers for data centers. The report includes a SWOT analysis illustrating the issues facing the United States and requiring the attention of the Department of Defense (DoD) and the Department of Commerce (DoC). Recommendations are also provided to ensure that U.S. AI data centers are resilient and competitive globally.

HPC data center markets now demand components with the highest processing and communication rates (low latencies and high bandwidth, often both simultaneously) and highest capacities with extreme requirements for advanced packaging solutions at both the component level and system level [1,2]. Insatiable demands have been projected for heterogeneous compute, memory, storage, and data communications. Interconnect has become one of the most important pillars of compute for these systems. If the taxonomy of interconnects within data centers are examined, the off-package interconnects include network interconnects (4-8 lanes) which are latency tolerant, and load store interconnects (hundreds of lanes) which are latency sensitive. On package die to die Interconnects for load store (tens of thousands of lanes) are extremely latency sensitive. Load store interconnects should be thought of as a continuum and needs to scale from die to package to board level and finally to node level [3].

Electronics manufacturers globally report that their growth is constrained by an inability to recruit, onboard, retain, and upskill workers. This white paper presents a holistic view of the workforce challenges facing the industry and outlines IPC’s approach to developing industry—wide solutions that are engaging, scalable, efficient, and effective. At the heart of IPC’s approach is an unprecedented and ambitious initiative to create career pathways within this dynamic industry.

In an ever-more automated, digitized, and connected world, electronic system design has evolved from a former novel concept to an absolute necessity; it now encompasses several highly skilled and valued professions and provides a source of inspiration for many creative and ingenious people. Striving for excellence is a deeply ingrained human trait, and since the world of electronic design is still a human endeavor, the encompassing term “Design for Excellence” is used. This term, however, continues to provoke lively discussion and debate about its scope and meaning in every professional forum. The purpose of this paper is to explore and elaborate on several elements of the Design for Excellence methodology, with the goal of re-thinking how it might be further defined, applied, and achieved in the full ecosystem of electronic design. This whitepaper provides a high-level
exploration of the full “Silicon-to-Systems” ecosystem, examines the justification and implications of an Authoritative Source of Truth (ASOT), discusses the need for synergy between building blocks of electronic systems, takes a deeper dive into the subject of Design Rules, and emerges back to the surface by discussing the paradigm of true Design for Manufacturability.

Complex integrated systems (CIS) combine different types of functions—e.g., digital, analog, optical, micro-mechanical, power-related, structural—in a single system to ensure the best solution for the product and its end market. These are systems where:
1. For any given function, the specialized implementation technology and/or material system that provides the best performance at the right cost point is chosen. 
2. The different function implementations are integrated using the most appropriate interconnect and packaging solutions. 
3. Multiscale, holistic approaches are essential for design, simulation, assembly and test across the complete, sustainable product lifecycle, including manufacturing.
4. The resulting supply chains intelligently integrate the capabilities, expertise, and business practices used across wafer fabrication, chip/die packaging, module assembly and final system assembly. 

Many critical CIS applications that are now scaling into mass adoption, require increasing levels of integration across heterogeneous technologies. In aggregate, the market opportunities associated with CIS solutions have significant annual growth rates and companies are investing tens of billions of dollars to address these. Examples of new market opportunities include massive wireless broadband with 5G mm Wave systems, augmented/virtual/mixed reality (AR/VR/MR) devices, and advanced driver assistance systems (ADAS) in passenger vehicles. 
These demands are inspiring product designers to seek innovative designs, materials, and assembly processes to manufacture CIS-enabled products. These products are designed by leveraging a rich portfolio of active components, mechanical elements, and functional materials from optics to electronics to micro-mechanics.
 

Companies that offer CIS design engineering and manufacturing services have developed or acquired the necessary capabilities and equipment to offer complete product assembly, potentially at one facility. Manufacture for CIS-based products often require precision placement, high tolerance control, scalable manufacturing, and/or a cleanroom processing environment. The logistics for assembly of CIS products minimizes the handling and shipping of unfinished modules from location to location, thereby minimizing supply chain disruption and potentially reducing the product carbon footprint. 

The impacts of realizing CIS products extends beyond manufacturing into other aspects of the product 
life cycle, including design, test, sustainability and smart manufacturing. 

The electronics manufacturing industry finds itself today in a moment of change driven by the convergence of multiple independent factors that all conspire to greatly challenge the status quo of how electronics factories are managed:

- The increasing complexity of electronics manufacturing (and decreasing feature sizes) pushing the limits of what is feasible with current process controls
- The blurring of the line between semiconductor and circuit manufacturing caused by increased usage of advanced packaging and chip-scale integration technologies
- Rapidly increasing demand for additional electronics manufacturing caused by the adoption of high-performance computing (HPC), electric vehicles and consumer electronics
- Geopolitical pressures forcing the relocation of manufacturing to areas without an abundant expert labor pool
- A changing workforce with fewer deep experts being asked to oversee many more lines and factories

These pressures are coming at a time when rapid advancements in new artificial intelligence (AI) technologies are challenging conventional notions about what is possible to automate and potentially providing a new path forward for the industry. In many ways, the frontier of electronics manufacturing is increasingly resembling the semiconductor 
industry one or two decades ago in terms of the level of process complexity and required automation. This analogy of the electronics manufacturing industry following the path of the semiconductor industry provides the ability to look into the possible future of electronics manufacturing by examining how the semiconductor industry has evolved over the past 20 years. There are many insights one could draw from this analogy, but the focus of this white paper is on one of the most conspicuous differences one will see, even today, when walking the floor of an electronics factory and a semiconductor fab. No semiconductor fab runs without data analytics at the core of the manufacturing process. In fact, semiconductor fabs incorporate a complete Fault Detection and Classification (FDC) system as a core operational tool next to the manufacturing execution system (MES) where rich data from all process steps is automatically collected, unified, enriched and analyzed in real time to flag problems and enable high-yield production. In stark contrast, very few, if any, EMS factories employ a similar level of data analytics today simply because it was not historically necessary and the industry focus instead has been on direct automation of manual production tasks without reimagining how the factory was run overall. Looking forward, however, many of the biggest opportunities facing the industry are about shifting how factories are managed using data instead of just replacing humans with robots doing the exact same work

Environmental sustainability is a driving force for both consumers and businesses across many industries; the electronic sector is no exception. Sustainability reporting standards are being developed and implemented. Requirements are already in place in many countries regarding restricted materials, energy utilization, extended life requirements, recyclability, and end-oflife management. The industry must be ready to meet or exceed these requirements if we are to be good stewards of the planet. This paper provides an overview of several environmental sustainability matters, tools/mitigation systems, recommendations, and useful resources for the electronics industry.
 

The whitepaper Maximizing Returns: The ROI of Training in Electronics Manufacturing aims to provide a comprehensive analysis of how evaluating the return on investment (ROI) of training programs can optimize workforce performance and drive profitability in the electronics manufacturing industry. This paper explores both the direct and indirect benefits of training, discusses the importance of accurately tracking costs, and outlines best practices for maximizing ROI through tailored training, continuous learning, and management involvement.